
"""simple docstring"""

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
SCREAMING_SNAKE_CASE_            =    logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class   _UpperCAmelCase				:
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default="cifar10"  ,      metadata={"help": "Name of a dataset from the datasets package"}				)
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=_A  ,      metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}				)
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=_A  ,      metadata={"help": "The column name of the images in the files."}				)
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(default=_A  ,      metadata={"help": "A folder containing the training data."}				)
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(default=_A  ,      metadata={"help": "A folder containing the validation data."}				)
      __SCREAMING_SNAKE_CASE :				Optional[float]				   =						field(
          default=0.15  ,      metadata={"help": "Percent to split off of train for validation."}				)
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						field(
          default=_A  ,      metadata={
              "help": (
                  "For debugging purposes or quicker training, truncate the number of training examples to this "
                  "value if set."
              )
          }  ,      )
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						field(
          default=_A  ,      metadata={
              "help": (
                  "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                  "value if set."
              )
          }  ,      )




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       {}
          if self.train_dir is not None:
              UpperCAmelCase          =       self.train_dir
          if self.validation_dir is not None:
              UpperCAmelCase          =       self.validation_dir
          UpperCAmelCase          =       data_files if data_files else None

@dataclass
class   _UpperCAmelCase				:
      __SCREAMING_SNAKE_CASE :				str				   =						field(
          default=_A  ,      metadata={
              "help": (
                  "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
              )
          }  ,      )
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=_A  ,      metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}				)
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=_A  ,      metadata={
              "help": (
                  "Override some existing default config settings when a model is trained from scratch. Example: "
                  "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
              )
          }  ,      )
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=_A  ,      metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}				)
      __SCREAMING_SNAKE_CASE :				str				   =						field(
          default="main"  ,      metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}  ,      )
      __SCREAMING_SNAKE_CASE :				str				   =						field(default=_A  ,      metadata={"help": "Name or path of preprocessor config."}				)
      __SCREAMING_SNAKE_CASE :				bool				   =						field(
          default=_A  ,      metadata={
              "help": (
                  "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                  "with private models)."
              )
          }  ,      )
      __SCREAMING_SNAKE_CASE :				float				   =						field(
          default=0.75  ,      metadata={"help": "The ratio of the number of masked tokens in the input sequence."}				)
      __SCREAMING_SNAKE_CASE :				bool				   =						field(
          default=_A  ,      metadata={"help": "Whether or not to train with normalized pixel values as target."}				)

@dataclass
class   _UpperCAmelCase				(		_A				):
      __SCREAMING_SNAKE_CASE :				float				   =						field(
          default=1E-3  ,      metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}				)




def      lowercase__    (       lowerCAmelCase					:       List[str]			)			->   Any:






    """simple docstring"""




    UpperCAmelCase          =       torch.stack([example['pixel_values'] for example in examples]			)
    return {"pixel_values": pixel_values}




def      lowercase__    (       )			->   int:






    """simple docstring"""




    UpperCAmelCase          =       HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)			)
    if len(sys.argv			) == 2 and sys.argv[1].endswith('.json'			):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        UpperCAmelCase          =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
    else:
        UpperCAmelCase          =       parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry('run_mae'			,					lowerCAmelCase			,					lowerCAmelCase			)

    # Setup logging
    logging.basicConfig(
        format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,					datefmt='%m/%d/%Y %H:%M:%S'			,					handlers=[logging.StreamHandler(sys.stdout			)]			,					)

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    UpperCAmelCase          =       training_args.get_process_log_level()
    logger.setLevel(lowerCAmelCase			)
    transformers.utils.logging.set_verbosity(lowerCAmelCase			)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + F"distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}"			)
    logger.info(F"Training/evaluation parameters {training_args}"			)

    # Detecting last checkpoint.
    UpperCAmelCase          =       None
    if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
        UpperCAmelCase          =       get_last_checkpoint(training_args.output_dir			)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
            raise ValueError(
                F"Output directory ({training_args.output_dir}) already exists and is not empty. "
                'Use --overwrite_output_dir to overcome.'			)
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'			)

    # Initialize our dataset.
    UpperCAmelCase          =       load_dataset(
        data_args.dataset_name			,					data_args.dataset_config_name			,					data_files=data_args.data_files			,					cache_dir=model_args.cache_dir			,					use_auth_token=True if model_args.use_auth_token else None			,					)

    # If we don't have a validation split, split off a percentage of train as validation.
    UpperCAmelCase          =       None if """validation""" in ds.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split			,					lowerCAmelCase			) and data_args.train_val_split > 0.0:
        UpperCAmelCase          =       ds["""train"""].train_test_split(data_args.train_val_split			)
        UpperCAmelCase          =       split["""train"""]
        UpperCAmelCase          =       split["""test"""]

    # Load pretrained model and image processor
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    UpperCAmelCase          =       {
        """cache_dir""": model_args.cache_dir,
        """revision""": model_args.model_revision,
        """use_auth_token""": True if model_args.use_auth_token else None,
    }
    if model_args.config_name:
        UpperCAmelCase          =       ViTMAEConfig.from_pretrained(model_args.config_name			,					**lowerCAmelCase			)
    elif model_args.model_name_or_path:
        UpperCAmelCase          =       ViTMAEConfig.from_pretrained(model_args.model_name_or_path			,					**lowerCAmelCase			)
    else:
        UpperCAmelCase          =       ViTMAEConfig()
        logger.warning('You are instantiating a new config instance from scratch.'			)
        if model_args.config_overrides is not None:
            logger.info(F"Overriding config: {model_args.config_overrides}"			)
            config.update_from_string(model_args.config_overrides			)
            logger.info(F"New config: {config}"			)

    # adapt config
    config.update(
        {
            'mask_ratio': model_args.mask_ratio,
            'norm_pix_loss': model_args.norm_pix_loss,
        }			)

    # create image processor
    if model_args.image_processor_name:
        UpperCAmelCase          =       ViTImageProcessor.from_pretrained(model_args.image_processor_name			,					**lowerCAmelCase			)
    elif model_args.model_name_or_path:
        UpperCAmelCase          =       ViTImageProcessor.from_pretrained(model_args.model_name_or_path			,					**lowerCAmelCase			)
    else:
        UpperCAmelCase          =       ViTImageProcessor()

    # create model
    if model_args.model_name_or_path:
        UpperCAmelCase          =       ViTMAEForPreTraining.from_pretrained(
            model_args.model_name_or_path			,					from_tf=bool('.ckpt' in model_args.model_name_or_path			)			,					config=lowerCAmelCase			,					cache_dir=model_args.cache_dir			,					revision=model_args.model_revision			,					use_auth_token=True if model_args.use_auth_token else None			,					)
    else:
        logger.info('Training new model from scratch'			)
        UpperCAmelCase          =       ViTMAEForPreTraining(lowerCAmelCase			)

    if training_args.do_train:
        UpperCAmelCase          =       ds["""train"""].column_names
    else:
        UpperCAmelCase          =       ds["""validation"""].column_names

    if data_args.image_column_name is not None:
        UpperCAmelCase          =       data_args.image_column_name
    elif "image" in column_names:
        UpperCAmelCase          =       """image"""
    elif "img" in column_names:
        UpperCAmelCase          =       """img"""
    else:
        UpperCAmelCase          =       column_names[0]

    # transformations as done in original MAE paper
    # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
    if "shortest_edge" in image_processor.size:
        UpperCAmelCase          =       image_processor.size["""shortest_edge"""]
    else:
        UpperCAmelCase          =       (image_processor.size["""height"""], image_processor.size["""width"""])
    UpperCAmelCase          =       Compose(
        [
            Lambda(lambda lowerCAmelCase			: img.convert('RGB'			) if img.mode != "RGB" else img			),
            RandomResizedCrop(lowerCAmelCase			,					scale=(0.2, 1.0)			,					interpolation=InterpolationMode.BICUBIC			),
            RandomHorizontalFlip(),
            ToTensor(),
            Normalize(mean=image_processor.image_mean			,					std=image_processor.image_std			),
        ]			)

    def preprocess_images(lowerCAmelCase					:       Optional[int]			):
        UpperCAmelCase          =       [transforms(lowerCAmelCase			) for image in examples[image_column_name]]
        return examples

    if training_args.do_train:
        if "train" not in ds:
            raise ValueError('--do_train requires a train dataset'			)
        if data_args.max_train_samples is not None:
            UpperCAmelCase          =       ds["""train"""].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
        # Set the training transforms
        ds["train"].set_transform(lowerCAmelCase			)

    if training_args.do_eval:
        if "validation" not in ds:
            raise ValueError('--do_eval requires a validation dataset'			)
        if data_args.max_eval_samples is not None:
            UpperCAmelCase          =       (
                ds["""validation"""].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
            )
        # Set the validation transforms
        ds["validation"].set_transform(lowerCAmelCase			)

    # Compute absolute learning rate
    UpperCAmelCase          =       (
        training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
    )
    if training_args.base_learning_rate is not None:
        UpperCAmelCase          =       training_args.base_learning_rate * total_train_batch_size / 256

    # Initialize our trainer
    UpperCAmelCase          =       Trainer(
        model=lowerCAmelCase			,					args=lowerCAmelCase			,					train_dataset=ds['train'] if training_args.do_train else None			,					eval_dataset=ds['validation'] if training_args.do_eval else None			,					tokenizer=lowerCAmelCase			,					data_collator=lowerCAmelCase			,					)

    # Training
    if training_args.do_train:
        UpperCAmelCase          =       None
        if training_args.resume_from_checkpoint is not None:
            UpperCAmelCase          =       training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            UpperCAmelCase          =       last_checkpoint
        UpperCAmelCase          =       trainer.train(resume_from_checkpoint=lowerCAmelCase			)
        trainer.save_model()
        trainer.log_metrics('train'			,					train_result.metrics			)
        trainer.save_metrics('train'			,					train_result.metrics			)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        UpperCAmelCase          =       trainer.evaluate()
        trainer.log_metrics('eval'			,					lowerCAmelCase			)
        trainer.save_metrics('eval'			,					lowerCAmelCase			)

    # Write model card and (optionally) push to hub
    UpperCAmelCase          =       {
        """tasks""": """masked-auto-encoding""",
        """dataset""": data_args.dataset_name,
        """tags""": ["""masked-auto-encoding"""],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**lowerCAmelCase			)
    else:
        trainer.create_model_card(**lowerCAmelCase			)




def      lowercase__    (       lowerCAmelCase					:       Union[str, Any]			)			->   Any:






    """simple docstring"""




    main()


if __name__ == "__main__":
   main()





from __future__ import annotations







def 							A__       (    lowerCamelCase    )	->						int:
				UpperCamelCase_:				Optional[Any]						=    len(lowerCamelCase    ) // 2

				# choose the middle 3 elements
				UpperCamelCase_:				Tuple						=    lst[m - 1 : m + 2]

				# if middle element is peak
				if three[1] > three[0] and three[1] > three[2]:
								return three[1]

				# if increasing, recurse on right
				elif three[0] < three[2]:
								if len(lst[:m]    ) == 2:
												m -= 1
								return peak(lst[m:]    )

				# decreasing
				else:
								if len(lst[:m]    ) == 2:
												m += 1
								return peak(lst[:m]    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()






